Book Image

Deep Learning with Keras

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras

By: Antonio Gulli, Sujit Pal

Overview of this book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Using pre-trained embeddings

In general, you will train your own word2vec or GloVe model from scratch only if you have a very large amount of very specialized text. By far the most common use case for Embeddings is to use pre-trained embeddings in some way in your network. The three main ways in which you would use embeddings in your network are as follows:

  • Learn embeddings from scratch
  • Fine-tune learned embeddings from pre-trained GloVe/word2vec models
  • Look up embeddings from pre-trained GloVe/word2vec models

In the first option, the embedding weights are initialized to small random values and trained using backpropagation. You saw this in the examples for skip-gram and CBOW models in Keras. This is the default mode when you use a Keras Embedding layer in your network.

In the second option, you build a weight matrix from a pre-trained model and initialize the weights of your embedding layer with this weight matrix. The network will update these weights using backpropagation, but the model will...